Presently, process facilities (e.g., a manufacturing plant, a mineral or crude oil refinery, etc.) are managed using distributed control systems. Contemporary control systems include numerous modules tailored to control or monitor various associated processes of the facility. Conventional means link these modules together to produce the distributed nature of the control system. This affords increased performance and a capability to expand or reduce the control system to satisfy changing facility needs.
Process facility management providers, such as HONEYWELL, INC., develop control systems that can be tailored to satisfy wide ranges of process requirements (e.g., global, local or otherwise) and facility types (e.g., manufacturing, refining, etc.). A primary objective of such providers is to centralize control of as many processes as possible to improve an overall efficiency of the facility. Each process, or group of associated processes, has certain input (e.g., flow, feed, power, etc.) and output (e.g., temperature, pressure, etc.) characteristics associated with it.
In recent years, model predictive control ("MPC") techniques have been used to optimize certain processes as a function of such characteristics. One technique uses algorithmic representations to estimate characteristic values (represented as parameters, variables, etc.) associated with them that can be used to better control such processes. In recent years, physical, economic and other factors have been incorporated into control systems for these associated processes. Examples of such techniques are described in U.S. Pat. No. 5,351,184 entitled "METHOD OF MULTIVARIABLE PREDICTIVE CONTROL UTILIZING RANGE CONTROL;" U.S. Pat. No. 5,561,599 entitled "METHOD OF INCORPORATING INDEPENDENT FEEDFORWARD CONTROL IN A MULTIVARIABLE PREDICTIVE CONTROLLER;" U.S. Pat. No. 5,574,638 entitled "METHOD OF OPTIMAL SCALING OF VARIABLES IN A MULTIVARIABLE PREDICTIVE CONTROLLER UTILIZING RANGE CONTROL;" and U.S. Pat. No. 5,572,420 entitled "METHOD OF OPTIMAL CONTROLLER DESIGN OF MULTIVARIABLE PREDICTIVE CONTROL UTILIZING RANGE CONTROL" (the "'420 Patent"), all of which are commonly owned by the assignee of the present invention and incorporated herein by reference for all purposes.
Generally speaking, one problem is that conventional efforts, when applied to specific processes, tend to be non-cooperative (e.g., non-global, non-facility wide, etc.) and may, and all too often do, detrimentally impact the efficiency of the process facility as a whole. For instance, many MPC techniques control process variables to predetermined set points. Oftentimes the set points are a best estimate of a value of the set point or set points. When a process is being controlled to a set point, the controller may not be able to achieve the best control performances, especially under process/model mismatch.
To further enhance the overall performance of a control system, it is desirable to design a controller that deals explicitly with plant or model uncertainty. The '420 Patent, for example, teaches methods of designing a controller utilizing range control. The controller is designed to control a "worst case" process. An optimal controller for the process is achieved and, if the actual process is not a "worst case process," the performance of the controller is better than anticipated.
There are a number of well known PID "tuning" formulas, or techniques, and the most common, or basic, PID algorithm includes three known user specified tuning parameters (K, .tau..sub.1, .tau..sub.2) whose values determine how the controller will behave. These parameters are determined either by trial and error or through approaches that require knowledge of the process. Although many of these approaches, which are commonly algorithms, have provided improved control, PID controller performance tuned by such algorithms usually degrades as process conditions change, requiring a process engineer to monitor controller performance. If controller performance deteriorates, the process engineer is required to "re-tune" the controller.
Controller performance deteriorates for many reasons, although the most common cause is changing dynamics of the process. Since PID controller performance has been related to the accuracy of the process model chosen, a need exists for a PID controller that allows for such uncertainty by accounting for changing system dynamics and, desirably, by incorporating the same before any tuning parameters are calculated. A further need exists for a means to extend the above-described MPC techniques into PID controller design techniques.